






"""simple docstring"""


import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
		from sagemaker import Session, TrainingJobAnalytics
		from sagemaker.huggingface import HuggingFace




@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""   ,     """False"""						)						) is not True   ,     reason="""Skipping test because should only be run when releasing minor transformers version"""   ,     )
@pytest.mark.usefixtures("""sm_env"""						)
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]						)
class 						__UpperCamelCase     (     unittest.TestCase						):
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Any:
											if self.framework == "pytorch":
																	subprocess.run(
																	    f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()     , encoding='utf-8'     , check=UpperCAmelCase     , )
											assert hasattr(self     , 'env'   )
					def        UpperCAmelCase__ (							self							:	Tuple     , UpperCAmelCase							:	str   )	->       Optional[int]:
											lowerCAmelCase   :List[str]						=			f"""{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}"""
											# distributed data settings
											lowerCAmelCase   :List[Any]						=			{'smdistributed': {'dataparallel': {'enabled': True}}} if self.script != 'run_ddp.py' else None

											# creates estimator
											return HuggingFace(
											    entry_point=self.script     , source_dir=self.env.test_path     , role=self.env.role     , image_uri=self.env.image_uri     , base_job_name=UpperCAmelCase     , instance_count=UpperCAmelCase     , instance_type=self.instance_type     , debugger_hook_config=UpperCAmelCase     , hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}     , metric_definitions=self.env.metric_definitions     , distribution=UpperCAmelCase     , py_version='py36'     , )
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	int   )	->       int:
											TrainingJobAnalytics(UpperCAmelCase   ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""   )
					@parameterized.expand([(2,)]   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, Any]   )	->       List[Any]:
											# create estimator
											lowerCAmelCase   :Union[str, Any]						=			self.create_estimator(UpperCAmelCase   )

											# run training
											estimator.fit()

											# result dataframe
											lowerCAmelCase   :List[str]						=			TrainingJobAnalytics(estimator.latest_training_job.name   ).dataframe()

											# extract kpis
											lowerCAmelCase   :Any						=			list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']   )
											lowerCAmelCase   :str						=			list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']   )
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											lowerCAmelCase   :Tuple						=			(
											    Session().describe_training_job(estimator.latest_training_job.name   ).get('TrainingTimeInSeconds'     , 99_9999   )
											)

											# assert kpis
											assert train_runtime <= self.results["train_runtime"]
											assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy   )
											assert all(t <= self.results['eval_loss'] for t in eval_loss   )

											# dump tests result into json file to share in PR
											with open(f"""{estimator.latest_training_job.name}.json"""     , 'w'   ) as outfile:
																	json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss}     , UpperCAmelCase   )







"""simple docstring"""


from math import loga


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if a < 0:
												raise ValueError('Input value must be a positive integer' )
						elif isinstance(a__     ,      a__ ):
												raise TypeError('Input value must be a \'int\' type' )
						return 0 if (a == 0) else int(loga(a & -a ) )


if __name__ == "__main__":
		import doctest

		doctest.testmod()